Data Science: Impacting Infrastructure Investment Decisions

Assistant Professor Daniel Sheldon

I’m currently engaged in a research project sponsored by the Massachusetts Department of Transportation to assess the vulnerability of road networks in Western Massachusetts to disruptions caused by extreme weather events. This is part of a larger interdisciplinary project at UMass that is building holistic models of road and river networks—including factors such as water flow, infrastructure condition, and ecological connectivity—to provide information to help improve the resilience of road networks and the health of river networks. Our pilot project analyzes the 665 square mile portion of the Deerfield River watershed in Western Massachusetts to model the extent to which emergency services will be disrupted if flooding renders key road segments unpassable. We are developing optimization algorithms to select infrastructure maintenance and upgrade projects to minimize this disruption in the future.

Road-stream crossings are a growing area of concern for the health of road and river networks. In particular, culverts are ubiquitous and aging infrastructure elements, which, if they do not function properly, disrupt ecological connectivity in rivers and streams and risk failing and making roads impassable during floods.

Road failures during extreme weather events cause major disruptions to commerce and public services such as emergency medical services (EMS). Investing to improve infrastructure prior to a disaster is much more cost-effective than responding in a disaster relief scenario. The state Department of Transportation (DOT) sponsored a project to address the problem, which had several goals:

develop an innovative systems-based approach to improve the assessment prioritization, planning, protection and maintenance of roads and road-stream crossings

As part of this project, we addressed a key question: what is the best way to invest infrastructure dollars prior to a disaster?

We are addressing the problem in two phases. First, we assessed the potential of each road-stream crossing in the network to disrupt road network functionality—as measured by EMS response times—if it fails. Key tasks included: mapping “first responder” services (e.g., police and fire stations, hospitals), collecting and analyzing historical EMS data, using scenario analysis to “replay” old EMS incidents in different versions of the network after simulated failures, ranking of road-stream crossings and road segments based on disruption potential, and map-based visualization of results. The results of this phase of the project provide information about the disruption potential of individual culverts to MassDOT to guide maintenance and infrastructure planning.

Second, we are developing efficient optimization algorithms to help invest maintenance dollars to optimize the resilience of road networks. For this part of the project, each culvert is assigned a probability of failure, which will be based on modeling of the condition and geomorphic vulnerability done in other parts of the broader project. For each culvert, maintenance and repair actions can be performed to reduce the failure risk. The goal is to select which maintenance and repair actions to perform, given a fixed budget, to minimize the expected EMS response times after a flooding event.

We created a fast algorithm for this stochastic optimization problem by designing a novel sampling technique and a novel primal-dual procedure. Our method performs nearly optimally in benchmarks and is much more scalable than existing algorithms. These tools will influence decision making relative to road maintenance and lead to improved access to emergency medical services during natural disasters. We will soon be speaking with the DOT and the Massachusetts Emergency Management Agency to discuss next phases.

Our analytical platform can be applied more broadly to optimize the resilience of networks, including communication networks, social networks, financial networks, and habitat networks. Access more information about Daniel Sheldon’s research here.